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Member of Technical Staff — Research Engineering, Evaluation

Causal Labs· Research
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San Francisco, SingaporeFull-timePosted 2mo ago

About the role

Role focused on building a central evaluation framework for a Large Physics foundation Model (LPM), including pipelines, metrics, and tools to measure and compare model quality across teams. Located in San Francisco or Singapore, on-site, full-time.

Responsibilities

  • Design and build a central, reusable evaluation framework that every model and every team runs through
  • Implement evaluation pipelines, benchmark suites, and baselines that make model quality measurable and comparable across efforts
  • Build the visualization and dashboard tools that turn raw results into shared, actionable understanding for the whole team
  • Establish sound statistical methodology for evaluation, so teams can distinguish real improvements from noise
  • Partner with research and domain teams to translate what "good" means in each domain into standardized, automated metrics

Qualifications

  • Strong software engineering skills and experience building data or evaluation pipelines at scale
  • Experience turning research or model outputs into metrics, benchmarks, and visualizations that teams rely on
  • Solid grasp of probability and statistics, with the judgment to design evaluations that measure what they claim to
  • Full-stack range: comfortable building both backend pipelines and the frontend tools people read results in
  • Owns deliverables end-to-end, from collecting requirements to autonomously driving execution

Skills

Required:Software engineeringMetrics designStatistics / probabilityFull-stack developmentProject ownership
Last updated Oct 2, 2026. Interested? Apply on the company site.

About Causal Labs

Towards general causal intelligence

Causal Labs is building a Large Physics foundation Model (LPM) to learn causality through physics and weather

causalityphysicsweatherAIfoundation model

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